rtestim: Time-varying reproduction number estimation with trend filtering

• Published in: PLoS computational biology 2024-08, Vol.20 (8), p.e1012324
• Main Authors: Liu, Jiaping, Cai, Zhenglun, Gustafson, Paul, McDonald, Daniel J
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Basic Reproduction Number; Communicable Diseases - epidemiology; Computational Biology - methods; Computer Simulation; Disease transmission; Epidemiological Models; Epidemiologists; Forecasts and trends; Humans; Medical research; Medicine, Experimental; Methods; Models, Statistical; Poisson Distribution; Sentinel health events; Software
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1012324

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approach that solves a convex optimization problem-Poisson trend filtering-using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim.